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A Flexible Framework For Using NLP In XR

A Flexible Framework For Using NLP In XR

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International Conference on Extended Reality (XR Salento)

作者： Miller, Andrew Kalafatis, Stavros Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9783031717062;9783031717079

The increasing use of Extended Reality (XR) brings a need for more advanced Human-computer Interaction (HCI) technologies to allow for intuitive and robust collaboration. However, challenges arise when also ensuring interactions are both natural and effective, particularly when incorporating flexible verbal communication. Prior research has explored many multi-modal interaction technologies, yet there exists a need for a framework to allow for human-computer communication in specifically XR applications. This work proposes a novel framework that can incorporate advanced means of Natural Language Processing (NLP) to handle flexible verbal communication while ensuring computer interpretation is robust. Drawing from prior research that identified limitations of information given in XR visuals and challenges in using widely available NLP tools, the proposed framework uses a rule-enforced command extraction pipeline to ensure consistent human-like processing, while preserving the flexibility of more open-domain verbal dialogue. The design employs a sequence of NLP techniques for spoken utterance analysis, language specific rules to extract usable game commands, and verification on the structured command with a user-reinforced hypergraph method of data storage. The framework and its processing pipeline was evaluated using a wide-range of human-like phrasings of commands deemed representative of verbalized instructions for human-computer collaboration in an XR context. Framework performance was then compared against a Large Language Model (LLM) that was customized to extract commands using the same rules. These results serve to showcase the potential of the proposed framework through demonstrated examples and early results while also noting areas of needed performance enhancements.

关键词： eXtended Reality (XR) Human-computer Interaction (HCI) Natural Language Processing (NLP)

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Radiomics-Enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer 1

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3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2022

作者： Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman School of Computer Science The University of Sydney Sydney Australia Med-X Research Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation. Unfortunately, without segmentation masks, these methods will take the whole image as input, such that makes them difficult to focus on tumor regions and potentially unable to fully leverage the prognostic information within the tumor regions. In this study, we propose a radiomics-enhanced deep multi-task framework for outcome prediction from PET/CT images, in the context of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR 2022). In our framework, our novelty is to incorporate radiomics as an enhancement to our recently proposed Deep Multi-task Survival model (DeepMTS). The DeepMTS jointly learns to predict the survival risk scores of patients and the segmentation masks of tumor regions. Radiomics features are extracted from the predicted tumor regions and combined with the predicted survival risk scores for final outcome prediction, through which the prognostic information in tumor regions can be further leveraged. Our method achieved a C-index of 0.681 on the testing set, placing the 2nd on the leaderboard with only 0.00068 lower in C-index than the 1st place. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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CrossViT-ReID: Cross-Attention Vision Transformer for Occluded Cloth-Changing Person Re-Identification 17th

CrossViT-ReID: Cross-Attention Vision Transformer for Occlu...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Nguyen, Vuong D. Mantini, Pranav Shah, Shishir K. Quantitative Imaging Lab Department of Computer Science University of Houston Houston United States

ISBN: (纸本)9789819608843

Real-world Person Re-Identification (Re-ID) presents severe challenges like occlusions and clothing changes, making traditional Re-ID methods fail. Existing occluded Re-ID methods struggle with cloth-changing scenarios, while current cloth-changing Re-ID methods do not explicitly address occlusions. To this end, we propose CrossViT-ReID, the first framework for the challenging yet practical Occluded Cloth-Changing Person Re-ID task. We perform occlusion synthesis to expose the model to real-world occlusion variations, and capture cloth-invariant body shape modality from silhouettes. The key to success of CrossViT-ReID lies in our novel cross-modality collaborative training strategy which is capable of mining the complementary relationship between appearance and shape adaptively under occlusions, clothing changes, or bad lighting conditions. Specifically, we devise two identical ViT-based branches. One branch takes in holistic appearance and occluded shape, aiming to focus on appearance when shape is noisy. Meanwhile, occluded appearance and holistic shape are inputs to the other branch, aiming to attend to shape when appearance is partly unobservable. Cross-attention fusion then makes the two branches exchange beneficial information and complement each other. After being trained, our framework is able to amplify the most informative cues when facing ambiguity caused by in-the-wild Re-ID challenges, thus significantly enhancing Re-ID accuracy. Extensive experiments demonstrate the superiority of CrossViT-ReID on both cloth-changing Re-ID and occluded Re-ID datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reamers

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Vision-Language Pretraining for Variable-Shot Image Classification 31st

Vision-Language Pretraining for Variable-Shot Image Classifi...

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31st International Conference on Multimedia Modeling

作者： Papadopoulos, Sotirios Ioannidis, Konstantinos Vrochidis, Stefanos Kompatsiaris, Ioannis Patras, Ioannis Informat Technol Inst Thessaloniki Greece Queen Mary Univ London London England

ISBN: (纸本)9789819620708;9789819620715

Contrastively pretrained vision-language models (VLMs) such as CLIP have shown impressive zero-shot classification performance without any classification-specific training. They create a common embedding space by contrastively pretraining an image and a text encoder to align positive image-text pairs and repel negative pairs. Then zero-shot classification of an image can be performed by measuring the cosine similarities between the image embedding and embeddings of texts that describe the classes. However, relevant works do not address the scenario in which few image examples for some (not all) classes are available. In this novel task which we term variable-shot (v-shot) classification, these models fail due to the embedding space modality gap, i.e. the fact that image-to-image similarities are higher than image-to-text ones. To this end, we propose to enable v-shot capabilities in pre-trained VLMs with minimal training complexity by re-projecting embeddings of frozen pre-trained image encoders using a shallow network, RectNet, which we train both with the standard CLIP contrastive loss function, as well as a novel modality alignment loss function specifically constructed to bridge the modality gap. Finally, we introduce three v-shot classification benchmarks, on which the proposed architecture achieves 32.22%, 29.58% and 45.15% increases in top-1 classification accuracy respectively.

关键词： variable-shot classification v-shot classification vision-language pretraining computer vision

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BoT-FaceSORT: Bag-of-Tricks for Robust Multi-face Tracking in Unconstrained Videos 17th

BoT-FaceSORT: Bag-of-Tricks for Robust Multi-face Tracking ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghyeon Ju, Chan-Yang Kim, Gun-Woo Lee, D.-H. Department of Applied Artificial Intelligence Hanyang University Ansan Korea Republic of Department of Computer Science and Engineering Gyeongsang National University Jinju Korea Republic of

ISBN: (纸本)9789819609000

Multi-face tracking (MFT) is a subtask of multi-object tracking (MOT) that focuses on detecting and tracking multiple faces across video frames. Modern MOT trackers adopt the Kalman filter (KF), a linear model that estimates current motions based on previous observations. However, these KF-based trackers struggle to predict motions in unconstrained videos with frequent shot changes, occlusions, and appearance variations. To address these limitations, we propose BoT-FaceSORT, a novel MFT framework that integrates shot change detection, shared feature memory, and an adaptive cascade matching strategy for robust tracking. It detects shot changes by comparing the color histograms of adjacent frames and resets KF states to handle discontinuities. Additionally, we introduce MovieShot, a new benchmark of challenging movie clips to evaluate MFT performance in unconstrained scenarios. We also demonstrate the superior performance of our method compared to existing methods on three benchmarks, while an ablation study validates the effectiveness of each component in handling unconstrained videos. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Kalman filters

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A Lightweight Method to Survey with Protecting Privacy yet Maintaining Accuracy 18th

A Lightweight Method to Survey with Protecting Privacy yet...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yuxuan Di, Xinyu Liu, Ningbo Zhang, Xianchao Ren, Wei School of Computer Science China University of Geosciences Wuhan China Department of Computer Science University of Toronto TorontoONM5S 1A1 Canada International College Chongqing University of Technology Chongqing China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing University Jiaxing China Ministry of Education Chengdu China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China

ISBN: (纸本)9783031714665

In a survey, a lot of questions and answers are listed and participators will select one of the answers for each question. Sometimes, participators may not be willing to select the preferred one item in the answers because the item may damage the privacy of themselves. If this question is not answered, then it reveals that the question or the answers are sensitive, which also damages the privacy. Besides, each participator may have distinct privacy concerns even for the same questions and answers, thus the privacy-aware questions and answers cannot be determined and be removed in advance. Thus, the dilemma is how to select the answers for privacy-aware questions yet maintain the utility of the answering results in the survey. In this paper, we propose a general method to protect the various privacy in the investigation without the knowledge of privacy to be protected in advance. The main idea is that participators can randomly select answers if the privacy is the concern for themselves in the answers for this question. Our method is lightweight so as to be convenient in practices. We extensively analyze the accuracy of survey results to justify that our method can protect the privacy yet maintain the utility of the investigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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Docking and Post-Processing of 1 Million Molecules from the CNCL Database in Search of SARS-CoV-2 Mpro Inhibitors 10th

Docking and Post-Processing of 1 Million Molecules from the ...

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10th Russian Supercomputing Days Conference, RuSCDays 2024

作者： Sulimov, Alexey Kutov, Danil Ilin, Ivan Sulimov, Vladimir Dimonta Ltd Moscow Russia Research Computer Center Lomonosov Moscow State University Moscow Russia

ISBN: (纸本)9783031784583

COVID-19 has triggered an intense worldwide search for low molecular weight inhibitors of a number of target proteins of the SARS-CoV-2 coronavirus responsible for the pandemic. However, the problem of creating an effective anti-coronavirus drug is still far from a final solution. We recently successfully applied our SOL docking program and subsequent quantum-chemical postprocessing to discover SARS-CoV-2 inhibitors in the Voronezh State University database containing approximately 20 000 organic compounds. Inspired by this success, in this work, we applied a developed methodology to screen the Chinese National Compound Library, CNCL, containing about 1 million molecules. The aim of the screening was to find inhibitors of the main protease of SARS-CoV-2. Two-stage docking of the whole CNCL using the SOL program followed by quantum chemical post-processing of 3 000 ligands with best docking characteristics resulted in 17 most promising and chemically diverse compounds for experiments testing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hydrogen bonds

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The Introduction of AI in Healthcare: A Multi-layered Issue 23rd

The Introduction of AI in Healthcare: A Multi-layered Iss...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Quaranta, Marinella Amantea, Ilaria Angela Molinari, Marianna Computer Science Department Università degli Studi di Torino Turin Italy Università degli Studi di Bologna Bologna Italy Azienda Sanitaria Locale ASL TO3 Collegno Turin Italy

ISBN: (纸本)9783031734960

The updated text of the Artificial Intelligence Act (AI Act) has raised some questions about tools using Artificial Intelligence in different fields. In particular, the specimen of AI technology for medical purposes has now become a concerning topic for the introduction of additional legal and procedural requirements. The AI Act is just one of several pieces of legislation regulating AI medical tools, as many have already been established (e.g. Medical Device Regulation) and the intersection of requirements and obligations needs to be assessed. With multiple layers of relevant provisions about AI medical tools, our main objective is to create a practical guideline for the introduction of AI tools in healthcare systems by ensuring their legal compliance. We also aim to show legal short circuits related to AI medical tools to have a comprehensive perspective on the issue of introducing AI in Healthcare. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Going Beyond Passages: Readability Assessment for Book-Level Long Texts 23rd

Going Beyond Passages: Readability Assessment for Book-Leve...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Li, Wenbiao Sun, Rui Zhang, Tianyi Wu, Yunfang MOE Key Laboratory of Computational Linguistics Peking University Beijing China School of Software and Microelectronics Peking University Beijing China School of Computer Science Peking University Beijing China

ISBN: (纸本)9789819783663

Readability assessment for book-level long text is widely needed in real educational applications. However, most of the current researches focus on passage-level readability assessment and little work has been done to process ultra-long texts. In order to process the long sequence of book texts better and to enhance pretrained models with difficulty knowledge, we propose a novel model DSDR, difficulty-aware segment pre-training and difficulty multi-view representation. Specifically, we split all books into multiple fixed-length segments and employ unsupervised clustering to obtain difficulty-aware segments, which are used to re-train the pretrained model to learn difficulty knowledge. Accordingly, a long text is represented by averaging multiple vectors of segments with varying difficulty levels. We construct a new dataset of Graded Children’s Books to evaluate model performance. Our proposed model achieves promising results, outperforming both the traditional SVM classifier and several popular pretrained models. In addition, our work establishes a new prototype for book-level readability assessment, which provides an important benchmark for related research in future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Difficulty knowledge Readability Assessment Ultra-long texts processing

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Scaling Up Reasoning from Conditional Belief Bases 16th

Scaling Up Reasoning from Conditional Belief Bases

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16th International Conference on Scalable Uncertainty Management, SUM 2024

作者： Beierle, Christoph Haldimann, Jonas Sanin, Arthur Schwarzer, Leon Spang, Aron Spiegel, Lars-Phillip von Berg, Martin Knowledge-Based Systems Faculty of Mathematics and Computer Science FernUniversität in Hagen Hagen58084 Germany Institute of Logic and Computation TU Wien Vienna1040 Austria University of Cape Town and CAIR Cape Town7700 South Africa

ISBN: (纸本)9783031762345

This paper introduces the online reasoning platform InfOCF-Web 2.0 that provides easy access to implementations of various inference methods for conditional belief bases. We present an overview of the realization of the inductive inference operator s p-entailment, system Z, c-inference, and system W. In order to address the fact that the possible worlds to be taken into account grow exponentially with the propositional signature over which the conditionals in the belief base are defined, the implementations employ SAT and Partial MaxSAT concepts and use the power of current SAT and SMT solvers. Our evaluation shows that each of the four inference operators can handle belief bases over signatures containing more than 100 variables and with more than 100 conditionals. Thus, InfOCF-Web 2.0 scales up nonmonotonic reasoning from conditionals to a new dimension because apart from the implementations now available in InfOCF-Web 2.0, there is no other implementation of an inference operator for conditional belief bases for which such problem sizes are feasible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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